A Book That Will Never Be Read | Hacker News Reader

A Book That Will Never Be Read

forbetterscience.com

56 pointsjruohonen23 comments

Here's where we talk about whether AI is taking our jobs; it took me 5 years and three supervisors to produce a doctoral thesis that nobody will ever read.

My wife actually had her paper cited recently, and that sat dormant for a decade. Hopefully yours does too, it’s quite a funny situation to find yourself in.

Though I’m not quite sure how that ties into AI replacement theory.

You say that as if that isn't the point

Hurts to read. But I guess the scientific publications were already headed that way and AI accelerates mostly the unscrupulous. Now it's going to be harder and harder to separate the wheat from the chaff, and I fear AI will not help here, unless it's about purely theoretic results. I guess a web of trust will be more and more important in the future. Not a great solution though.

This is what Religion offers. I set of moral principles and community that you can expect to conform to those principles

The values proclaimed by religion and actual behavioral expectations of community tend to be at odds quite often.

AI is pretty capable of detecting meaningless fluff though. If anything it could actually help to find the actually promising papers.

> meaningless fluff

You haven't read academic papers in a while...

You haven't read the good ones it seems. The number of bad papers published is irrelevant for progress. It is just important that there are good ones and that the researchers who can advance the state of the art on top of these good papers can find them in the sea of bad papers.

Thanks for the thoughtful reply.

> can find them in the sea of bad papers

How to find the good ones, as an amateur in a given field?

I fear you need to bootstrap by finding people you trust not to produce slop. Check what they use/build upon. The research other people use or improve is more likely to be ok. If you do this for multiple independent people in a field, the things (methods/algorithms/data/...) used by many others should be good. Science usually converges over time.

Things taught at universities not affiliated with the authors should also be safe.

It's neither quickly done, nor 100% bulletproof, but I guess science never was. Also you'll lag behind at least a year (assuming a 1 year cycle, from something being published to being used in next years publications), but as an amateur that should be worth it.

I think this is a problem that existed even before. If you look closely, AI just accelerates everything—both the pros and the cons. It seems to be an inherent problem with the academic system itself.

Signals are inherently bound to be distorted. The "legitimate methods" conceived by humans and social signals are easy to manipulate. Because public data like the number of published papers and diplomas act as strong social signals, they end up being distorted. This is known as Goodhart's Law.

The problem is that if this persists long-term, there will eventually be a shortage of personnel to quantitatively evaluate papers, and society probably won't hire these researchers. There is also no incentive to hire them, as verification rarely leads to direct profit. People tend to want to be the starting point of a discipline; they generally do not like checking other people's work.

As a result, academia will increasingly be validated solely by "which lab, which advising professor, and what peer network certified you." The value of the offline world will rise, simply because digital costs have become too cheap.

Soon, research quality and social status will likely merge again, resembling the Middle Ages. Even if we assume someone has actual research capabilities, if evaluators do not invest enough time in verification, the system will inevitably revolve around the more prestigious labs.

I suspect there will be a flood of research applying AI, but the topics of these studies will likely be quite narrow and localized (it would be nice to have statistics on this). I believe the overall diversity of topics explored across academia will diminish.

For this reason, I think existing networks will only grow stronger.

I feel that any solutions to this problem will go awry. I doubt self-purification is even possible anymore. Established researchers protect the monopoly power of publishers, while those existing publishers generally just want to cut costs. The authority of these publishers must be protected by the established researchers who have already secured their positions.

Next, the system that used to manipulate the number of papers will probably shift into a system that manipulates network reputation. The competition to get into good labs and the strategies to secure co-authorships with famous researchers will simply become the new means of competition.

This is a problem academics have talked about since at least the 1990s. You get what you incentivize, and we incentivize papers. Not innovation, not discovery, not new ideas, but papers.

Then what solutions has academia proposed? From what you're saying, it's been over 30 years. In my country there's a saying: "A monk cannot shave his own head." Has it been solved? Or has this proverb, as expected, been proven true? I'm curious.

I think there's an equally large meta-incentive problem: the people who have power within the system are generally incentivised not to rock the boat, and the people who would like to rock the boat generally don't have power. This bind could be broken by external pressure (from government, itself incentivised by popular pressure) but it's very hard for most outsiders to understand and evaluate proposed changes, so even if the will was there, I don't know how much would be accomplished.

As an interested outsider myself, it's pretty depressing seeing how high the risk:reward ratio seems to be for people within academia who are driven to clean it up and call out bad actors, and how high the reward:risk ratio is for those who are willing to cut corners (or just plain cheat, if they do it in a minimally competent way).

That is true in most systems!

I definitely don't think it's unique to academia. I think some systems are more tethered to reality than others, though -- some are more like a ship, where even though the various crew members' interests are far from perfectly aligned, they all have a strong shared interest in not drowing, and most of them have a shared interest in getting to the intended destination efficiently. So although the system might become brutal and unequal, it's unlikely to shift toward a set of processes and norms that don't actually work at all.

My impression is that large portions of academia (and not just the most obvious candidates) are too close to the other extreme, with nothing to guarantee that indefinite cargo culting is not a stable equilibrium. And even when people notice this and get mad about it, they're more likely to flip the political sign of the problem than to actually fix it. But I hope this is overly cynical.

Modern Academia has largely always rooted very deeply in the self and a sense of ego, and not within progression of the human knowledge base. This is in large part due to an “Us vs Them” mentality that has been brewed over several decades even before the World Wars. The consequences are that individual output and prestige are placed higher than meaningful contribution, and that people do not seek truth but rather seek self-gratification.

In my experience this is more prevalent and can be hidden better in the soft “sciences” like history or psychology, but you can even find it in fields like theoretical physics. In fact, a perfect example lies in the recent solving of the 700+ math proofs via OpenAI. The mathematicians were not upset that an LLM did the work, they were upset that a human could not take the credit. This is because that credit is the currency within academic circles, and OpenAI “deprived” those researchers of putting their name on the paper.

As for the reputation of networks, we already see that taking place and the fraud capable in that, too. A letter of recommendation means little when they can be bought or obtained dishonestly.

Ultimately, I believe that much like in the Middle Ages a Renaissance will form of independent non-Academia scholars who will push the field. I already follow a handful of YouTubers who I respect more than any Ph.D’d pencil-pushers.

For a small list: Nighthawkinlight, Tech Ingredients, Cody’s Lab, and Hyperspace Pirate

> "a Spanish fellow named Álex Escolà-Gascón, Professor for Exorcism and Divine Apparitions, fabricated a bizarre clinical trial to prove the existence of immortal soul and life after death."

There are days where I feel the title "Director of Exorcism and Divine Apparitions" would be appropriate for what I actually do. :-)

Not surprised it reached books. In Graphite's Common Crawl sample, ~50% of new web articles have been mostly AI-written since late 2024, and still ~50% as of May 2026. The odd part is the plateau. Chart: aiwords.si

Feels like this has been going on for 50 years. The AI acceleration just made it more visible.

Before it was artisanal non-replicable, p-hacked, publish or perish motivated, politically biased slop. Now we have some AI sugar sprinkled on top.

I look to AI as an unprecedented tool to clear out the slop and find the well supported work. But no, the slop pipeline pays its way and finding a way around it is not very important it seems.

I don’t think we’re ready for the absolute warp-speed break down of reliable references this process of slop creation, citation, and publication that is going to occur.

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